Selective laser melting (SLM) is considered to be a highly significant additive manufacturing (AM) technology, with the capacity to produce complex shapes that would be difficult to achieve using other methods. However, the broad application of this method is limited by problems like harmful microstructures and porosity, especially during the processing of aluminum alloys. Laser shock peening (LSP) provides a promising approach to reduce the adverse effects linked to aluminium SLM. This research examines how a critical LSP parameter, specifically the number of impacts, influences AlSi10Mg parts produced by SLM. The results were assessed with porosity, microstructure, and microhardness. Results show a 72% reduction in porosity. Furthermore, microstructural analysis revealed discernible grain refinement, accompanied by enhanced hardness. Tensile testing further confirmed the effectiveness of LSP, showing increases in both ultimate tensile strength and yield strength. These results suggest that LSP can effectively address the limitations of the SLM process for demanding applications when used as a post-processing technique.

Electrodeposition, a fundamental technique in materials science, has been developed to produce nanostructured coatings with improved mechanical, chemical, and physical properties. This study encompasses a systematic review of approaches based on prediction and optimization models at electrodeposition processes applicable to various materials. It discusses the theoretical background, such as mechanisms of nucleation and growth, and the key factors influencing the characteristics of coatings. The paper reviews traditional thermodynamic models as well as advanced data-driven techniques, with a special focus on machine learning methods, such as artificial neural networks (ANNs), dynamic ANNs (DANNs), and support vector machines (SVMs). The models are validated by the prediction of properties such as hardness, adhesion, and corrosion resistance. We also compare optimization strategies, such as genetic algorithms, particle swarm optimization, and their hybrids, to analyze their capability to improve both coating quality and process efficiency. The development discussed in this research is representative of the increased usage of AI and computational approaches, which allow for process control in real time, decreasing experimental costs and designing performance coatings. At the same time, new trends like sustainable electrodeposition, electrochemical 3D printing, or intersection with additive manufacturing are highlighted as well. This study highlights that predictive and optimization models have the potential to significantly impact the development of electrodeposition technologies targeted for industrial uses.

This research focused on the die exit width, ram speed and temperature effects on extruded Al 6063 alloy mechanical properties. It is a multiple approach that involves numerical, experimental and simulation methods in optimizing the extrusion process. The Q-Form was used in extruded sample flow stress and strain distribution analysis. The result revealed die exit width as the parameter with the most significant influence on Al 6063 alloy tensile strength and hardness, followed by extrusion temperature and then ram speed. The die width increase from 6mm to 8mm yields 73.5 % and 75.8 % tensile strength and hardness increase. The optimized process parameters predicted by the model are a speed of 16.2567 mm/s, a temperature of 526.334 °C, and a die exit diameter of 7.1862 mm, which yields a tensile strength of 151.031 MPa and a hardness of 183.644 HB, respectively. Based on Qform findings, the sample extruded using these optimal parameters yielded uniform metal flow products with low stress concentration. The research enables deep knowledge into the extrusion parameters and mechanical properties relationship, leading to aluminum alloy hot extrusion process optimization. This research has contributed to the more effective and efficient extrusion process development that can be applied in many aluminum extrusion industries. The product quality can be improved through optimized process parameters, thereby reducing the cost of production and boosting the extrusion process's overall efficiency.

The aim of this research is to obtain the minimum spring back value in bending metal plates using a press tool with a squishing process for the case of forming aluminium plates on S-shaped clothes hanger products with a 90o angle. The problem faced in bending metal plates is the occurrence of spring back which has more serious consequences if for example aircraft door products which must have accurate product dimensions so that they can be opened and closed tightly without any air or rainwater leaks. The research method includes simulation with Simufact and Solidwork softwares on a 2.8 mm thick aluminium plate with varying squishing depths, namely 0.1 mm, 0.2 mm, and 0.3 mm after the bending step, measuring the resulting spring back, making a radius optimal punch from the simulation results, testing the bending of the plate with a press tool, measuring the spring back value that occurs, analyzing the spring back results, and drawing conclusions. Results from research with an optimal punch radius on an S-shaped clothes hanger product with a 90o angle with a value of 1.5 mm, an optimal squishing depth of 0.1 mm on 2.8 mm thick aluminium material with a width of 19 mm producing a spring back of 0.058o with compressive force worth 3009.6 kg.

To achieve lightweighting of the aircraft landing gear door hinge under opening and closing loads, a topological optimization model based on the variable density method was established, with structural stiffness as the constraint and minimum mass as the objective. The hinge was redesigned according to the optimized configuration, and the stiffness and strength of the design area were validated. The original aluminum alloy hinge material was replaced with lower-density polyetheretherketone (PEEK), which can be fabricated into complex structures via fused deposition modeling (FDM), thereby enhancing design freedom for topology optimization. However, FDM-printed PEEK's mechanical properties are influenced by printing parameters. This study conducted tensile tests on PEEK specimens printed with different FDM parameters (e.g., layer height, platform temperature, and infill pattern). The optimal printing parameters were determined as 0.1 mm layer height, 120 °C platform temperature, and tetrahedral infill. Subsequently, the best-performing specimens underwent heat treatment, and the effects of different annealing parameters on tensile strength were investigated. The results showed that annealing at 330 °C for 2 hours yielded the highest strength improvement. Furthermore, the hinge's loading conditions during door operation were simulated via multi-body dynamics analysis, while static simulations under peak loads identified stress concentration areas. Topology optimization was then performed to minimize material usage while maintaining mechanical performance, achieving the lightweight goal.

The boundary conditions are idealized in mechanics of materials. However, small imperfections may exist which deviate the conditions from being ideal. The non-ideal boundary conditions are modeled using perturbations and applied to buckling problems of columns. A linear and a nonlinear model are treated separately. The effect of such conditions on the critical loads and buckling shapes are analytically calculated using the strained parameter perturbation method. Depending on the mode numbers and the physical parameters, the critical loads may either decrease or increase. The mode shapes of the buckling are also distorted by the imperfections in the support conditions.